Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (6): 1691-1697.DOI: 10.11772/j.issn.1001-9081.2017123013

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Compressive data gathering based on even clustering for wireless sensor networks

QIAO Jianhua1,2, ZHANG Xueying1   

  1. 1. School of Information Engineering, Taiyuan University of Technology, Taiyuan Shanxi 030024, China;
    2. School of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan Shanxi 030024, China
  • Received:2017-12-22 Revised:2018-02-09 Online:2018-06-10 Published:2018-06-13
  • Supported by:
    This work is partially supported by the Natural Science Foundation Project of Shanxi Province (2013011019-1).

基于均衡分簇的无线传感器网络压缩数据收集

乔建华1,2, 张雪英1   

  1. 1. 太原理工大学 信息工程学院, 太原 030024;
    2. 太原科技大学 电子信息工程学院, 太原 030024
  • 通讯作者: 张雪英
  • 作者简介:乔建华(1975-),女,山西吕梁人,副教授,博士研究生,主要研究方向:无线传感器网络、压缩感知;张雪英(1964-),女,河北行唐人,教授,博士,主要研究方向:语音信号处理、多媒体通信、物联网。
  • 基金资助:
    山西省自然科学基金资助项目(2013011019-1)。

Abstract: Compressive Data Gathering (CDG) using the combination of Compressed Sensing (CS) theory and sparse random projection for Wireless Sensor Network (WSN) can greatly reduce the amount of data transmitted over the network. Aiming at the unstable and unbalanced problems of the overall energy consumption of network caused by selecting the projection nodes randomly as cluster heads to collect data, two new compresseive data gathering methods of balanced projection nodes were proposed. For WSN with uniform distribution of nodes, an even clustering method based on spatial location was proposed. Firstly, the grids were evenly divided. Then, the projection nodes were selected in each grid for clustering according to the shortest distance principle. Finally, the intra-cluster data was collected by the projection nodes to the sink node for completing the data collection, so that the projection nodes were distributed evenly and the network energy consumption was balanced. For WSN with uneven distribution of nodes, an even clustering method based on node density was proposed. The locations and densities of nodes were taken into account together, for the grid with small number of nodes, the projection nodes were no longer selected, and the few nodes in the grid were allocated to the adjacent grids, which balanced the network energy and prolonged the network lifetime. The simulation results show that, compared with the random projection node method, the network lifetime of the proposed two methods is extended by more than 25%, and the number of remaining nodes can reach about 2 times in the middle stage of network running. The proposed two methods have better network connectivity and increase the overall network lifetime significantly.

Key words: Wireless Sensor Network (WSN), Compressed Sensing (CS), Compressive Data Gathering (CDG), random projection, clustering, sink node

摘要: 应用压缩感知(CS)理论结合稀疏随机投影的无线传感器网络(WSN)压缩数据收集(CDG)可以大大减少网络传输的数据量。针对随机选择投影节点作为簇头来收集数据导致网络整体能耗不稳定和不平衡的问题,提出两种平衡投影节点的压缩数据收集方法。对于节点分布均匀WSN,提出基于空间位置的均衡分簇法:首先,均匀划分网格;然后,在每个网格选举投影节点,依距离最短原则成簇;最后,由投影节点收集簇内数据到汇聚节点完成数据收集,从而使得投影节点分布均匀、网络能耗均衡。对于节点分布不均匀的WSN,提出基于节点密度的均衡分簇法:同时考虑节点的位置和密度,对节点数量少的网格不再选择投影节点,将网格内的少量节点分配到邻近的网格,从而平衡网络能量,延长网络寿命。仿真结果表明,与随机投影节点法相比,所提的两种方法的网络寿命均延长了25%以上,剩余节点数在网络运行中期均能达到2倍左右,具有更好的网络连通性,显著提高了整个网络的生命周期。

关键词: 无线传感器网络, 压缩感知, 压缩数据收集, 随机投影, 分簇, 汇聚节点

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